Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

Abdelsamea, Mohammed M.; Mohamed, Marghny H.; Bamatraf, Mohamed A.

doi:10.1177/1176935119857570

Cited by 18 publications

(13 citation statements)

References 12 publications

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“…The database used in this research are mammogram images from MIAS (Mammographic Image Analysis Society) database: 120 abnormal images (57 benign and 63 malignant) with a size of 1024 pixels x 1024 pixels and database obtained from UDIAT (Hospital de Sabadell, Spain) in the research of Tortajada et al [8] and RSPAD (Gatot Soebroto Army Central Hospital, Jakarta): 52 abnormal images (19 benign and 33 malignant). In addition, this research used public database DDSM (Digital Database for Screening Mammography) with 256 abnormal images consisting of 95 benign and 161 malignant tumors.…”

Section: Results and Discussion Databasementioning

confidence: 99%

“…(Gandhamal et al [2], Anand and Gayathri [12], Pawar, Talbar, and Dudhane [9], Mane and Kulhalli [6]) improve the quality of mammogram images at the preprocessing stage by increasing contrast and eliminating noise. Abdelsamea, Mohamed, and Bamatraf [8] carried out the process of cropping the background area on mammogram images as well.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…Accuracy results obtained by 86 % using Gabor Wavelet and obtain an accuracy of 89 % using DWT. (Abdelsamea et al, 2019) proposed an automated classification of malignant and benign breast cancer lesions using neural networks on digitized mammograms [8]. The classification process introduced a new neuron structure that obtained an accuracy value of 95.2 % using MIAS database with 118 abnormal images (64 benign and 54 malignant).…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

See 2 more Smart Citations

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2021

Computer Optics

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Breast cancer is a leading cause of death in women due to cancer. According to WHO in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25-30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.

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Section: Results and Discussion Databasementioning

confidence: 99%

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

See 1 more Smart Citation

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2021

Computer Optics

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“…Xin Shu et al [ 33 ] proposed a region-based pooling structure deep neural network for mammogram image classification. In [ 34 ], a novel deep neural network approach was presented with a mapping technique for the neuron structure for the classification of cancerous and noncancerous mammograms. To overcome the distance between the neurons, self-organizing map was used [ 35 ] to reduce the training time, and the classification task was performed with an artificial neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Rehman

Pei

et al. 2021

Sensors

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Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

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“…Besides, there is a process of removing noise, one of them using the median filter which can maintain the edge information. Mohammed M. Abdelsamea and Bamatraf (2019) perform the pre-processing process by cropping unwanted areas.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2020

MAS

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Breast cancer is one of the leading causes of death worldwide among women. According to GLOBOCAN Data, the International Agency for Research on Cancer (IARC), in 2012 there were 14.067.894 new cases of cancer and 8.201.575 deaths from cancer worldwide (Kementerian Kesehatan Republik Indonesia [KemenkesRI], 2015). Mammography is the most common and effective technique for detecting breast tumors. However, mammograms have poor image quality with low contrast. A Computer-Aided Detection (CAD) system has been developed to help radiologists effectively detect lesions on mammograms that indicate the presence of breast tumor. The feature extraction method in the CAD system is an important part of getting high accuracy results in classifying normal and abnormal breast tumors. By using the combination of 2D-Discrete wavelet transform and Gray-Level Co-Occurrence Matrix (GLCM) obtained an accuracy value of 100% on MIAS and UDIAT Database in classifying the presence of masses in the mammogram image and obtained an accuracy value of 93.8% for classifying normal, benign, and malignant. The proposed method has the potential to identify the presence of masses in the mammogram image as a decision support system to the radiologist.

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Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

Cited by 18 publications

References 12 publications

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

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